Traditional forecasting on time series is modeled by use of training data, which have been collected from occurrences in the past, based on various degrees of predictor functions. The training data include one or more independent predictor variables and at least one dependent variable determined as a predictor function of the one or more independent predictor variables, such that resulting time series forecasting models are enabled to estimate an unknown dependent variable from the one or more dependent predictor variables. Based on the degrees of the predictor functions, the time series forecasting models may be, but are not limited to, a linear curve regression model, a quadratic regression model, or a cubic regression model that displays respective characteristics in forecasting and varying accuracies. Many automated statistical software packages and programming languages, such as R, S, SAS® software, SPSS®, Minitab®, etc., are available for time series forecasting. (SAS is a registered trademark of SAS Institute Inc., Cary, N.C., USA; SPSS is a registered trademark of International Business Machines Corporation, Armonk, N.Y., USA; Minitab is a registered trademark of Minitab, Inc., in the United States and other countries.)